import pickle

with open("path/to/sample/data.pkl",'rb') as f:
          data = pickle.load(f)

import matplotlib.pyplot as plt #used for visualization purposes in this tutorial.

import pysiology
print(pysiology.__version__)


ECG = pysiology.sampledata.loadsampleECG() #load the sample ECG Signal
EMG = pysiology.sampledata.loadsampleEMG() #load the sample EMG Signal
GSR = pysiology.sampledata.loadsampleEDA() #load the sample GSR Signal

sr = 1000 #samplerate in Hz

#We can define the event in the way we prefer. 
#In this example I will use a 2 x nEvent matrix, containing the name of the event and the onset time.
events = [["A",10],
          ["B",20]]
eventLenght = 8 #lenght in seconds we want to use to compute feature estimation
results = {} #we will store the results in a dict for simplicity.
for event in events:
    startSample = sr * event[1] #samplerate of the signal multiplied by the onset of the event in s
    endSample = startSample + (sr * eventLenght) #Final sample to use for estimation
    results[event[0]] = {} #initialize the results
    results[event[0]]["ECG"] = pysiology.electrocardiography.analyzeECG(ECG[startSample:endSample],sr) #analyze the ECG signal
    results[event[0]]["EMG"] = pysiology.electromyography.analyzeEMG(EMG[startSample:endSample],sr) #analyze the EMG signal
    results[event[0]]["GSR"] = pysiology.electrodermalactivity.analyzeGSR(GSR[startSample:endSample],sr) #analyze the GSR signal